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Cost prediction of building projects using the novel hybrid RA-ANN model by Yali Wang; Jian Zuo; Min Pan; Bocun Tu; Rui-Dong Chang; Shicheng Liu; Feng Xiong; Na Dong is a Decision Sciences article available to read on EtoBox.

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## Purpose Accurate and timely cost prediction is critical to the success of construction projects which is still facing challenges especially at the early stage. In the context of rapid development of machine learning technology and the massive cost data from historical projects, this paper aims to propose a novel cost prediction model based on historical data with improved performance when only limited information about the new project is available. ## Design/methodology/approach The proposed approach combines regression analysis (RA) and artificial neural network (ANN) to build a novel hybrid cost prediction model with the former as front-end prediction and the latter as back-end correction. Firstly, the main factors influencing the cost of building projects are identified through literature research and subsequently screened by principal component analysis (PCA). Secondly the optimal RA model is determined through multi-model comparison and used for front-end prediction. Finally, ANN is applied to construct the error correction model. The hybrid RA-ANN model was trained and tested with cost data from 128 completed construction projects in China. ## Findings The results show tha

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Author

Yali Wang; Jian Zuo; Min Pan; Bocun Tu; Rui-Dong Chang; Shicheng Liu; Feng Xiong; Na Dong

Publisher

Emerald

Published

2023

Language

EN

Field

Decision Sciences (Social Sciences)